Prospect Theory for Online Financial Trading
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Liu, Yang-Yu, Jose C. Nacher, Tomoshiro Ochiai, Mauro
Martino, and Yaniv Altshuler. “Prospect Theory for Online
Financial Trading.” Edited by Tobias Preis. PLoS ONE 9, no. 10
(October 15, 2014): e109458.
As Published
http://dx.doi.org/10.1371/journal.pone.0109458
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Public Library of Science
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http://hdl.handle.net/1721.1/92479
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Prospect Theory for Online Financial Trading
Yang-Yu Liu1,2,3*., Jose C. Nacher4., Tomoshiro Ochiai5., Mauro Martino6, Yaniv Altshuler7
1 Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, United States of America, 2 Center for
Complex Network Research and Departments of Physics, Computer Science and Biology, Northeastern University, Boston, Massachusetts, United States of America,
3 Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America, 4 Department of Information Science, Faculty of
Science, Toho University, Funabashi, Chiba, Japan, 5 Department of Social Information Studies, Otsuma Women’s University, Tama-shi, Tokyo, Japan, 6 Center for
Innovation in Visual Analytics, Watson Research Center, IBM, Cambridge, Massachusetts, United States of America, 7 MIT Media Lab, Cambridge, Massachusetts, United
States of America
Abstract
Prospect theory is widely viewed as the best available descriptive model of how people evaluate risk in experimental
settings. According to prospect theory, people are typically risk-averse with respect to gains and risk-seeking with respect to
losses, known as the ‘‘reflection effect’’. People are much more sensitive to losses than to gains of the same magnitude, a
phenomenon called ‘‘loss aversion’’. Despite of the fact that prospect theory has been well developed in behavioral
economics at the theoretical level, there exist very few large-scale empirical studies and most of the previous studies have
been undertaken with micro-panel data. Here we analyze over 28.5 million trades made by 81.3 thousand traders of an
online financial trading community over 28 months, aiming to explore the large-scale empirical aspect of prospect theory.
By analyzing and comparing the behavior of winning and losing trades and traders, we find clear evidence of the reflection
effect and the loss aversion phenomenon, which are essential in prospect theory. This work hence demonstrates an
unprecedented large-scale empirical evidence of prospect theory, which has immediate implication in financial trading, e.g.,
developing new trading strategies by minimizing the impact of the reflection effect and the loss aversion phenomenon.
Moreover, we introduce three novel behavioral metrics to differentiate winning and losing traders based on their historical
trading behavior. This offers us potential opportunities to augment online social trading where traders are allowed to watch
and follow the trading activities of others, by predicting potential winners based on their historical trading behavior.
Citation: Liu Y-Y, Nacher JC, Ochiai T, Martino M, Altshuler Y (2014) Prospect Theory for Online Financial Trading. PLoS ONE 9(10): e109458. doi:10.1371/journal.
pone.0109458
Editor: Tobias Preis, University of Warwick, United Kingdom
Received April 23, 2014; Accepted September 1, 2014; Published October 15, 2014
Copyright: ß 2014 Liu et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The authors confirm that, for approved reasons, some access restrictions apply to the data underlying the findings. The trading data analyzed
in the work was not directly collected by the authors, but provided by an online trading company, according to their data privacy policy and their agreement with
MIT. Hence, due to both ethical and legal concerns, the authors cannot make the data public available without the permission of the company. However, upon
request, they can refer any researchers to that company, which provides access to past data (and specifically – the same data that was used for this research)
through their platform to academic researchers who sign an agreement with them (which is what the authors did). Hence it is totally possible for other
researchers to reach the data owner by contacting yanival@media.mit.edu.
Funding: This work was partially supported by NS-CTA sponsored by US Army Research Laboratory under Agreement No. W911NF-09-2-0053; DARPA under
Agreement No. 11645021; DTRA under Agreement No. HDTRA1-10-1-0100/BRBAA08-Per4-C-2-0033. The funders had no role in study design, data collection and
analysis, decision to publish, or preparation of the manuscript.
Competing Interests: Mauro Martino is an employee of IBM. This does not alter the authors’ adherence to PLOS ONE policies on sharing data and materials.
* Email: yyl@channing.harvard.edu
. These authors contributed equally to this work.
better understanding of collective human behavior in financial
markets [4–7]. In this work we focus on economic decision under
risk, a key subject of behavior economics [8]. Successful behavior
economic theories acknowledge the complexity of human
economic behavior and introduce models that are well grounded
in psychological research. For example, prospect theory is viewed
as the best available descriptive model of how people evaluate risk
[9–14]. Prospect theory states that people make decisions based on
the potential value of losses and gains rather than the final
outcome, and that people evaluate these losses and gains using
certain heuristics. Despite the fact that prospect theory offers many
remarkable insights and has been studied for more than three
decades, there exist very few large-scale empirical research and
most of the previous studies have been undertaken with micropanel data [15–21]. Moreover, there are relatively few well-known
and broadly accepted applications of prospect theory in economics
and finance [14]. The emergence of online social trading platforms
Introduction
We live life in the ‘‘big data’’ era. Many of our daily activities
such as checking emails, making mobile phone calls, posting blogs
on social media, shopping with credit cards and making financial
trading online, will leave behind our digital traces of various kinds
that can be used to analyze our behavior. The sudden influx of
data is transforming social sciences at an unprecedented pace
[1,2]. Indeed, we are witnessing the shift of social science research
paradigm from interviewing a few dozens of people with crafted
survey questionnaire to designing experiments involving millions
of subjects on social media [3].
The availability of huge amounts of digital data also prompts us
to rethink some fundamental perspectives of complex human
behavior. Recent studies have already illustrated the potential that
extensive behavioral data sets (e.g., Google trends, Wikipedia
usage patterns, and financial news developments) could offer us a
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Prospect Theory for Online Financial Trading
Fig. 1a). Among them, mirror trade has the highest chance
(&83%), much higher than that of single or copy trade. This
indicates that in average social trades (especially mirror trades)
indeed help traders win more frequently than non-social trades.
Interestingly, not all the trade types have positive average ROI (see
Fig. 1b). In fact, only mirror trade has positive average ROI
(&0:03%), i.e., it generates profit, consistent with previous results
[23]. In terms of ROI, social trades do not necessarily perform
better than non-social trade. We notice that copy trade even has
higher negative ROI than non-social trade, which simply implies
that copying someone based on past performance can be
dangerous.
Overall, mirror trade outperforms both single and copy trades.
Yet, the better performance of mirror trade comes at the price that
its winning trades have much lower ROI (<0.177%) than that of
copy and single trades (see Fig. 1c); while its losing trades have
significantly higher negative ROI (<20.9%) than that of copy and
single trades (see Fig. 1d). In other words, mirror trade typically
does not generate high profit for winning positions but generate
high loss for losing positions. Since mirror trade has very high
chance of winning, the average ROI of mirror trade turns out to
be positive. This implies that there is still much room to improve
our social trading experience.
To further understand the difference between social and nonsocial trade types, we calculate their duration distributions P(t)
(see Fig. 2). Here the duration t of a trade or position is defined to
be its holding time (in unit of millisecond), i.e., t : ~tclosed {topened
where topened and tclosed are the position opened and closed time,
respectively. Interestingly, P(t) displays similar fat-tailed distribution for all different trade types. There are very few positions that
were held for very long time (more than one month). Most of the
positions were held for less than half an hour. We also notice that
for losing positions, many of them are held for less than one
second, while for winning positions they are most likely held for
longer than one second. This might be due to the so-called bid-ask
spread. The price we can sell (bid) and the price we can buy (ask) is
different at each time point. It is almost impossible for traders to
overcome the spread within very short holding time interval (e.g.,
one second) by using online financial trading platforms. For
t[½103 ,105 , we find that for all different trade types P(t) of
positive trades is much lower than that of negative trades, i.e.,
winning probability is much less than 50% in this particular
holding time window. Although the duration distributions of
different trade types share many similar features, we do observe a
noticeable difference in the regime of tw105 , i.e., longer than one
minute. We find that for non-social trades with tw105 , P(t) of
positive trades is roughly the same as that of negative trades. In
other words, if the holding time of a non-social trade is longer than
one minute, the winning chance is about 50%. For copy trades
with t[(105 ,108 ), P(t) of positive trades is slightly higher than that
of negative trades. For mirror trades with t[½105 ,109 , P(t) of
positive trades is significantly higher than that of negative trades.
In other words, if the holding time of a mirror trade is longer than
one minute and less than one week, then its winning chance is
much higher than 50%.
We also calculate the trade duration as a function of the net
profit for different trade types (see Fig. 3). We draw the box-andwhisker plot of duration (t) for trades with net profit (p) binned
logarithmically. (For negative trades pv0, we bin them using DpD.)
We denote the median value of durations as tm . We find that for
all trade types tm shows asymmetric behavior: tm of losing
positions with loss {p is generally higher than that of winning
positions with profit p. This is a reflection of the so-called
and the availability of burgeoning volume of financial transaction
data of individuals help us explore the empirical aspect of prospect
theory to an unprecedented large-scale. Moreover, analyzing the
trading behavior at the individual level offers an excellent
opportunity to develop pragmatic financial applications of
prospect theory.
By harnessing the wisdom of the crowds to our benefit and gain,
social trading has been a revolutionary way to approach financial
market investment. Thanks to various Web 2.0 applications,
nowadays online traders can rely on trader generated financial
content as the major information source for making financial
trading decisions. This ‘‘data deluge’’ raises some new questions,
answers to which could further deepen our understanding of the
complexity of human economic behavior and improve our social
trading experience. For example, many social trading platforms
allow us to follow top traders, known as gurus or trade leaders, and
directly invest our money like they do. The question is then how to
identify those top traders. Analyzing their historical trading
behavior would be a natural starting point [22].
Analysis
The financial transaction data used in this work comes from an
online social trading platform for foreign exchanges and
commodities trading. This trading platform allows traders to take
both long and short positions, with a minimal bid of a few dollars
as well as leverage up to 400 times. The most important feature of
social trading platform is that each trader automatically has all
trades uploaded to the platform where trades can be displayed in a
number of statistical ways, such as by the amount of profit made.
Traders can then set their accounts to copy one or more trades
made by any other traders, in which case the social trading
platform will automatically execute the trade(s). Accordingly, there
are three types of trades: (i) Single (or non-social) trade: Trader A
places a normal trade by himself or herself; (ii) Copy trade: Trader
A places exactly the same trade as trader B’s one single trade; (iii)
Mirror trade: Trader A automatically executes trader B’s every
single trade, i.e., trader A follows exactly trader B’s trading
activities. Both (ii) and (iii) are hereafter referred to as social
trading.
There are about 3 million registered accounts in this online
social trading platform. Some of them are practice accounts, i.e.,
trading with virtual money. Our data are composed of over 28.5
million trades made by 81.3 thousand traders trading with real
money from June 2010 to October 2012. There are 31.8% single
trades, 0.6% copy trades and 67.6% mirror trades. Apparently,
social trading dominates over this trading platform during the time
window of our data. It will be desirable to learn how to select the
best traders to follow so that we can further improve our social
trading experience — a pragmatic motivation of our current work.
Quantitatively analyzing trading activities of traders within the
framework of behavior economics naturally fits the goal.
Ultimately, we would like to be able to predict potential winners
based on their historical trading behavior so that we can take full
advantage of the social trading paradigm.
Results
Social vs. Non-social trade
We first need to demonstrate if social trades really help. In
Fig. 1 we compare the fraction of winning trades (N+/N) and
return on investment (ROI (%) : ~netprofit=investment|100)
of the three different trade types. We find that all three trade types
have more than 50% chance to generate positive net profit (see
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Prospect Theory for Online Financial Trading
Figure 1. Performance comparison of different types of trades. (a) Fraction of positive trades. Mirror trade has the highest fraction of positive
trades. (b) Mean ROI. Mirror trade is the only trade type that has the positive SROIT. Here error bars mean the standard error of the mean (SEM). (c)
Mean ROI of positive trades. Mirror trade has the lowest SROIT for positive trades. (d) Mean ROI of negative trades. Mirror trade has the highest
negative SROIT for negative trades.
doi:10.1371/journal.pone.0109458.g001
zT
negative trades. (ii) Win-loss holding time ratio s : ~ St
St{ T, where
St+ T represents the average holding time of positive/negative
trades made by each trader. sw1 means that traders in average
hold positive position longer than negative position. (iii) Win-loss
SROIz T
, where SROI+ T represents the average
ROI ratio u : ~ SDROI
{ DT
ROI of positive/negative trades made by each trader. uw1 means
that traders in average have larger absolute ROI in positive trades
z
than in negative trades. (iv) Winning percentage w : ~ NzNzN
,
{
where N+ represents the number of positive/negative trades made
by each trader. ww1=2 simply means that traders in average have
larger chance of winning than losing a trade.
Note that if all traders trade pure randomly without any human
emotions we would expect that the distributions of all the four
metrics show symmetric behavior around r~1, s~1, u~1 and
w~1=2, respectively. Yet, in reality traders behave quite
differently from random (see Fig. 4). We find the risk-reward
distribution P(r) displays strongly asymmetric behavior around
r~1 (black dotted line in Fig. 4a). For rw1, P(r) follows a power
law over almost 3 decades, which means it is extremely difficult to
find traders with very large r; while for rƒ1, P(r) is almost a
constant, which means traders with rƒ1 are almost uniformly
distributed. By splitting the traders into two groups: winning and
losing traders (i.e., traders with final net profit or net loss) and
calculating their P(r) with appropriate normalization based on the
‘‘disposition effect’’: investors tend to sell financial assets whose
price has increased while keeping asserts that have dropped in
value [24–27]. In other words, investors are less willing to
recognize losses, but are more willing to recognize gains. This is a
typical irrational behavior that can be partially explained by the
‘‘loss aversion’’ phenomenon and the ‘‘reflection effect’’ in
prospect theory [18, 28–32]. We also notice clear differences
between mirror trade and the other two trade types. For both nonsocial and copy trades tm generally increases as DpD increases in
either positive or negative direction, and tm of losing trades
increases much faster as DpD increase than tm of winning trades
increases as p increase. While for mirror trade, tm increases very
slowly as p increases for positive positions. For mirror trades with
negative p, tm increases initially as DpD increases, but quickly
reaches a plateau. In other words, the disposition effect is lessened
in mirror trade. It has been shown that more experienced investors
are less affected by the disposition effect [33]. This might explain
the good performance of mirror trade.
Winning vs. Losing traders
To characterize the trading behavior of traders and identify
potential trade leaders, we introduce four behavioral metrics: (i)
Spz T
Risk-reward ratio r : ~ SDp
, where Sp+ T represents the average
{ DT
profit of positive/negative trades made by each trader. rw1 means
that traders in average gain more in positive trades than the loss in
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Figure 2. Duration distribution of different trade types. For each trade type, we further distinguish negative and positive trades based on
their net profit. The trades with zero net profit are negligible. The duration distributions of negative and positive trades are normalized according to
their corresponding occurrence. (a) All trades. (b) Non-social trades. (c) Copy trades. (d) Mirror trades.
doi:10.1371/journal.pone.0109458.g002
with very large u. Interestingly, for uƒ0:2, P(u) also follows a
power-law over almost 2 decades. We find P(u)’s of winning and
losing traders are also very different (see Fig. 4f). Winning traders’
u-range spans over ½10{1 ,300; while losing traders’ u-range is
given by ½5|10{4 ,300. For uvu ~2 (pink line), P(u) of losing
traders is significantly higher than that of winning traders; while
for uwu, it is in the opposite case. For uv0:06, almost all traders
are losing traders.
Note that a large portion of traders (85:2%) are losing traders
with final net loss. The fact that those losing traders typically have
rv1,sv1 and uv1 could be explained by the ‘‘loss aversion’’
phenomenon and the ‘‘reflection effect’’ in prospect theory as
follows. For positive positions, traders tend to be risk-averse and
will close the position quickly to take a small profit or a small ROI.
While for losing positions, traders tend to be risk-seeking and
reluctant to close the positions as quick as they should. Instead
they keep waiting and hoping to recover the loss. If indeed this
happens, then they become risk-averse and tend to close the
position quickly to take a small profit and result in a small ROI. If
unfortunately this does not happen, they waste not only valuable
time but also a lot of money, rendering large negative ROI. Thus,
the losing traders have to suffer from their irrational trading
behavior that can be described by prospect theory.
One may naively consider the winning percentage type of
behavioral metrics will help us identify gurus. Here we show it is
fractions of winning and losing traders (14.7% and 85.2%,
respectively), we find that the two groups behave in drastically
different ways (see Fig. 4b). Winning traders’ r-range spans over
½10{2 ,103 ; while losing traders’ r-range is given by ½10{4 ,101 .
The uniform P(r) for rv1 is largely due to losers; while the powerlaw of P(r) for rw10 is purely due to winning traders. We also
notice that for rvr ~4 (pink line), P(r) of losing traders is
significantly higher than that of winning traders; while for rwr , it
is in the opposite case.
We find the win-loss duration ratio distribution P(s) also
displays strongly asymmetric behavior around s~1 (black dotted
line in Fig. 4c). For sw1, P(s) almost follows a power law over 5
decades, which means it is extremely difficult to find traders with
very large s; while for sƒ1, P(s) decays very slowly as s decreases,
which means traders with sƒ1 are almost uniformly distributed.
This indicates that most traders hold losing positions for a longer
time than winning position, a typical disposition effect. Comparing
winning and losing traders’ P(s) is also interesting (see Fig. 4d).
Though their s-ranges are almost the same, we notice that for
svs ~100 (pink line), P(s) of losers is significantly higher than
that of winners; while for sws , it is in the opposite case.
The win-loss ROI ratio distribution P(u) shows a strong peak
around u~0:2 and a strongly asymmetric behavior around u~1
(black dotted line in Fig. 4e). For uw0:2, P(u) follows a power law
over 3 decades, which means it is extremely difficult to find traders
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Prospect Theory for Online Financial Trading
Figure 3. Disposition effect in different trade types. Here, we bin the net profit p of different trade types in logarithmic bins. (If pv0, we bin it
using DpD.) For the trades contained in each bin, we draw the box-and-whisker plot for their duration (t), representing the minimum, first quartile,
median, third quartiles, and maximum of the data in the bin. (a) All trades. (b) Non-social trades. (c) Copy trades. (d) Mirror trades.
doi:10.1371/journal.pone.0109458.g003
values (r , s , u ) of these behavioral metrics indicates the
importance of controlling human emotion to minimize the impact
of the reflection effect and the loss aversion phenomenon for better
trading performance.
not the case. Fig. 4g displays the winning percentage distribution
P(w), which is asymmetric around w~1=2. (Note that P(w) has
significant
peaks
around
some
rational
numbers
w~0,1=2,1=3,2=3,:::,1, which are due to traders who made very
few transactions.) Again, we find winning and losing traders’ P(w)
show dramatically different behavior (see Fig. 4h). For
wvw ~0:95, P(w) of losing traders is significantly higher than
that of winning traders; while for www winning traders
dominate. The value of w is so high that using it to select trade
leaders is almost infeasible. We also notice that for wvw , losing
traders actually dominate for a wide range of w. Yet, they are still
losing money eventually due to their very low risk-reward ratio r.
In other words, they win many times with small positive profit, but
once they lose they lose a lot.
In principle large values of those metrics do not imply net profit
at all. For example, traders with r&1 could be simply due to a few
trades with very large profit, but many trades with very small loss.
Yet, the above analysis yields three characteristic values
(r ~4, s ~100, u ~2) that can be used to statistically predict
potential winning traders with high probability. We admit that
those characteristic values may slightly depend on the particular
dataset or trading platform. We emphasize that the strategy of
using characteristic values of novel behavioral metrics to identify
potential winning traders should be applicable to general social
trading platforms. Furthermore, the existence of characteristic
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Discussion
The dynamics of financial trading is governed by individual
human decisions, which implies that the trading performance
could be significantly improved by understanding better the
underlying human behavior. In this work we systematically
analyze over 28.5 million trades made by 81.3 thousand traders
of an online financial trading platform. By analyzing and
comparing the performance of social and non-social trades,
winning and losing traders, we find clear evidences of the
reflection effect and the loss aversion phenomenon, which are
essential in prospect theory of behavior economics. Many losing
traders have very small risk-reward ratio (r), win-loss duration ratio
(s), and win-loss ROI ratio (u), suggest that we should develop new
trading strategies by systematically minimizing the impact of the
reflection effect and the loss aversion phenomenon, e.g., through
intentionally controlling s,u and/or r to fight over our human
nature and rationalize our trading behavior.
To provide traders many preferences in discovering gurus or
trade leaders, social trading platforms rank traders on many
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Prospect Theory for Online Financial Trading
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Prospect Theory for Online Financial Trading
Figure 4. Characterizing winning and losing traders based on historical trading behavior. (a, b) Distribution of risk-reward ratio
Spz T
). pz and p{ are the profit of winning positions and the loss of losing positions, respectively, of traders. (c, d) Distribution of win-loss
(r : ~ SDp
{ DT
zT
waiting time ratio (s : ~ St
St{ T). Here Stz T and St{ T are the average duration time of winning and losing positions, respectively, of traders. (e, f)
SROIz T
Distribution of win-loss ROI ratio (u : ~ SDROI
). ROIz and ROI{ are the ROI of winning and losing positions, respectively, of traders. (g, h)
{ DT
z
) of traders. Nz and N{ are the number of winning and losing positions, respectively, of traders.
Distribution of winning percentage (w : ~ NzNzN
{
doi:10.1371/journal.pone.0109458.g004
different metrics, e.g., popularity (number of followers), profitable
weeks, and the personal return of rate calculated from the
modified Dietz formula [34], etc. Those different metrics typically
yield different ranking lists, which effectively renders choosing the
gurus something of an inspirational affair or a delicate trick.
Moreover, traders should take into account the risks taken by these
gurus in order to obtain the returns that they make. Unfortunately,
not all the metrics rank the performance of traders on a riskadjusted basis. Here we propose three novel behavioral metrics
(risk-reward ratio r, win-loss holding time ratio s, and win-loss
ROI ratio u), which reveal the essence of prospect theory, the best
available descriptive model of how people evaluate risk in
behavioral economics. These metrics are defined for each trader
by comparing his/her typical behavior in winning and losing
trades, and hence are naturally risk-adjusted. Our analysis suggests
that these metrics can be used to statistically predict potential
winning traders, offering pragmatic opportunities to further
improve our social trading experience.
Acknowledgments
We thank Albert-László Barabási for insightful comments. This work was
partially supported by NS-CTA sponsored by US Army Research
Laboratory under Agreement No. W911NF-09-2-0053; DARPA under
Agreement No. 11645021; DTRA under Agreement No. HDTRA1-10-10100/BRBAA08-Per4-C-2-0033.
Author Contributions
Conceived and designed the experiments: YYL JCN TO. Performed the
experiments: YYL JCN TO. Analyzed the data: YYL JCN TO.
Contributed reagents/materials/analysis tools: YYL JCN TO. Wrote the
paper: YYL JCN TO. Reviewed the manuscript: MM YA.
References
17. Wu G, Zhang J, Abdellaoui M (2005) Testing Prospect Theories Using
Probability Tradeoff Consistency. J Risk Uncertain 30: 107–131.
18. Abdellaoui M, Bleichrodt H, Paraschiv C (2007) Loss aversion under prospect
theory: A parameter-free measurement. Manag Sci 53: 1659–1674.
19. Zhang W, Semmler W (2009) Prospect theory for stock markets: Empirical
evidence with time-series data. J Econ Behav Organ 72: 835–849.
20. Booij A, Praag BV, van de Kuilen G (2010) A parametric analysis of prospect
theory’s functionals for the general population. Theory Dec 68: 115–148.
21. Abdellaoui M, Bleichrodt H, Kammoun H (2011) Do financial professionals
behave according to prospect theory? An experimental study. Theory Dec 74:
411–429.
22. Barber B, Odean T (2000) Trading is hazardous to your wealth: The common
stock investment performance of individual investors. J Finance 55: 773–806.
23. Pan W, Altshuler Y, Pentland A (2012) Decoding social influence and the
wisdom of the crowd in financial trading network. In: SocialCom/PASSAT.
IEEE, pp. 203–209.
24. Shefrin H, Statman M (1985) The disposition to sell winners too early and ride
losers too long Theory and Evidence. J Finance 40: 777–790.
25. Weber M, Camerer C (1998) The disposition effect in securities trading: An
experimental analysis. J Econ Behav Organ 33: 167–184.
26. Odean T (1998) Are investors reluctant to realize their losses? J Finance 53:
1775–1798.
27. Barberis N, Xiong W (2009) What drives the disposition effect? an analysis of a
long-standing preference-based explanation. J Finance 64: 751–784.
28. Schmidt U, Traub S (2002) An Experimental Test of Loss Aversion. J Risk
Uncertain 25: 233–249.
29. Schmidt U, Zank H (2002) Risk Aversion in Cumulative Prospect Theory. Royal
Economic Society Annual Conference 2002 162, Royal Economic Society.
30. Schmidt U, Zank H (2005) What is Loss Aversion? J Risk Uncertain 30: 157–
167.
31. Brooks P, Zank H (2005) Loss Averse Behavior. J Risk Uncertain 31: 301–325.
32. Schmidt U, Zank H (2008) Risk Aversion in Cumulative Prospect Theory.
Manag Sci 54: 208–216.
33. Costa ND, Goulart M, Cupertino C (2013) The disposition effect and investor
experience. J Banking Finance 37: 16691675.
34. Dietz PO (2004) Pension Funds: Measuring Investment Performance (The
Spaulding Series). Macmillian.
1. Lazer D, Pentland A, Adamic L, Aral S, Barabsi AL, et al. (2009) Computational
social science. Science 323: 721–723.
2. Battiston S, Puliga M, Kaushik R, Tasca P, Caldarelli G (2012) Debtrank: Too
central to fail? financial networks, the fed and systemic risk. Sci Rep 2.
3. Bond RM, Fariss CJ, Jones JJ, Kramer ADI, Marlow C, et al. (2012) A 61million-person experiment in social influence and political mobilization. Nature
489: 295–298.
4. Moat HS, Curme C, Avakian A, Kenett DY, Stanley HE, et al. (2013)
Quantifying wikipedia usage patterns before stock market moves. Sci Rep 3:
1801.
5. Alanyali M, Moat HS, Preis T (2013) Quantifying the relationship between
financial news and the stock market. Sci Rep 3: 3578.
6. Preis T, Moat HS, Stanley HE (2013) Quantifying trading behavior in financial
markets using google trends. Sci Rep 3: 1684.
7. Krasny Y (2013) The effect of social status on decision-making and prices in
financial networks. In: Altshuler Y, Elovici Y, Cremers AB, Aharony N,
Pentland A, editors, Security and Privacy in Social Networks, Springer New
York. pp. 85–131.
8. Diamond P, Vartiainen H (2007) Behavioral Economics and Its Applications
Hardcover. Princeton University Press.
9. Kahneman D, Tversky A (1979) Prospect theory: An analysis of decision under
risk. Econometrica 47: 263–291.
10. Tversky A, Kahneman D (1981) The Framing of Decisions and the Psychology
of Choice. Science 211: 453–458.
11. Tversky A, Kahneman D (1992) Advances in prospect theory: Cumulative
representation of uncertainty. J Risk Uncertain 5: 297–323.
12. Wakker P, Tversky A (1993) An Axiomatization of Cumulative Prospect Theory.
J Risk Uncertain 7: 147–75.
13. Schmidt U, Starmer C, Sugden R (2008) Third-generation prospect theory.
J Risk Uncertain 36: 203–223.
14. Barberis N (2013) Thirty years of prospect theory in economics: A review and
assessment. J Econ Perspect 27: 173–196.
15. Camerer CF (1998) Prospect Theory in the Wild: Evidence From the Field.
Working Papers 1037, California Institute of Technology, Division of the
Humanities and Social Sciences.
16. Abdellaoui M (2000) Parameter-Free Elicitation of Utility and Probability
Weighting Functions. Manag Sci 46: 1497–1512.
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